2 research outputs found
Management of Thoracolumbar Spinal Fracture at Tertiary Care Hospitals: A Retrospective Study
No full textObjective: This study aimed to evaluate the fracture types, neurological deficits via the ASIA grading system, and to analyze fracture management strategies, post-fracture and surgical complications, in patients with thoracolumbar fractures.
Materials and Methods: A retrospective observational study was conducted on 114 TLS patients aged 16-75 years with known cases of TLS fracture. Data included demographics, mechanism of injury, radiographic investigations, fracture classifications, ASIA grades, treatment types, and outcomes. Descriptive statistics were used for analysis.
Results: The current study found that the majority of patients were male (64%) and aged 20-29 years (36%). Falls from height were the leading mechanism of injury. AO classification revealed a predominance of A1 (26.3%) and A2 (21.9%) fractures. ASIA A (complete neurological deficits) was found in 35.1% of patients, and ASIA E (no neurological deficit) in 31.6%. Conservative treatment was employed in 55.3% while 44.7% of patients underwent surgery, mostly through a posterior approach. Common complications included spinal cord compression (35.1%), pressure sores (21.9%), and neuropathic pain (13.2%). Overall, 70% of patients showed good recovery.
Conclusion: The study concluded that posterior surgical intervention is preferred, yielding favorable outcomes. Hence, early diagnosis and appropriate interventions are also crucial for minimizing complications and improving prognosis
BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley Data
No full textIn the field of agriculture, particularly within the context of machine learning applications, quality datasets are essential for advancing research and development. To address the challenges of identifying different mango leaf types and recognizing the diverse and unique characteristics of mango varieties in Bangladesh, a comprehensive and publicly accessible dataset titled “BDMANGO” has been created. This dataset includes images essential for research, featuring six mango varieties: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar, which were collected from different locations. The images were captured using the rear cameras of a Google Pixel 6a and an iPhone XR and were stored in 640 × 480 pixels resolution. Both sides of each mango leaf were photographed against white background to accurately reflect real-world scenarios in mango cultivation fields. The white background was specifically chosen to remove noise in image sample, allowing for accurate feature extraction by machine learning algorithms. This will ensure the trained model's efficacy in identifying a specific mango leaf while implemented alongside any segmentation algorithm. Additionally, image augmentation techniques such as rotation, horizontal flip, vertical flip, width shift, height shift, shear range, and zooming were applied to expand the dataset from 837 original images to a total of 6696 images (837 original image and 5859 augmented images). This expansion significantly enhances the dataset's utility for training, testing, and validating machine learning models designed for classifying mango leaf varieties, thereby supporting research efforts in this domain
